Echo planar imaging (“EPI”) is a rapid imaging technique that is widely employed in magnetic resonance imaging (“MRI”) for its ability to accurately image physiological states at extremely short time-scales (e.g., around 60 ms). Today, EPI is the primary method for acquiring functional MRI data of the brain, and is also widely used for diffusion and perfusion imaging throughout the body.
Although EPI is a key technology for both clinical and research applications, it is intrinsically vulnerable to specific image artifacts, such as geometric distortion due to spatially varying magnetic susceptibility across tissue that cause magnetic field inhomogeneity in the imaging volume, and “ghosting artifacts” due to stray magnetic fields and acquisition timing inaccuracies caused by scanner hardware imperfections.
The first of these two classes of artifact, geometric distortion, can be largely circumvented by employing accelerated parallel imaging techniques that are enabled by the use of arrays of surface coil detectors that are now standard components in modern MRI scanners. By skipping a portion of the image encoding process, the EPI data can be acquired more quickly, limiting the accrual of distortions with time. This missing information can be estimated in software during image reconstruction (e.g., using parallel imaging methods such as GRAPPA) through utilizing the complementary information provided by the various channels of the detector array.
The second artifact, image ghosting, can be removed through acquiring a small amount of additional data, known as phase navigators, that are used to estimate the phase errors which give rise to the ghosting. Once these navigators are acquired, a phase error correction can also be performed in software during image reconstruction. Both parallel imaging reconstruction and ghost correction require additional calibration data—fully-sampled reference data in the case of parallel imaging and line navigators in the case of ghost correction.
A recently developed technology known as simultaneous multislice (“SMS”) MRI can dramatically increase the temporal efficiency of EPI. Because in conventional EPI the stack of imaging slices are acquired sequentially, more image slices require longer imaging times. In SMS-EPI, multiple slices (e.g., 8 or 10) can be acquired simultaneously to reduce scan time. The simultaneously acquired slices can be separated during image reconstruction using parallel imaging techniques, such as the slice-GRAPPA technique.
An advantageous technology for obtaining high-quality images using the SMS-EPI technique is controlled aliasing or “CAIPIRINHA” (CAIPI for short), which tailors the sampling of the simultaneously-acquired slices in order to improve the separation of the slices during image reconstruction, which also provides improved signal-to-noise ratio (“SNR”). A variant of CAIPI used for SMS-EPI is known as blipped-CAIPI.
Because SMS-EPI is based on EPI, it still is vulnerable to the same geometric distortion and ghosting artifacts described above. SMS-EPI is compatible with conventional “in-plane” acceleration using techniques such as GRAPPA to reduce image distortions, although the use of “slice acceleration” (also referred to as “through-plane” acceleration) when acquiring multiple slices simultaneously can limit the use of in-plane acceleration because higher levels of in-plane acceleration restrict the amount of CAIPI shifting that can be performed.
Ghost correction in SMS-EPI is particularly challenging because each simultaneously acquired slice will typically have a distinct ghost level, necessitating a slice-specific ghost correction. But, the collapsing of the slices (as well as the navigators) prevents a direct ghost correction using standard methods. For this reason adaptations of conventional ghost correction approaches have been attempted, but the ghost artifacts cannot typically be completely eliminated. Nevertheless, the utilization of SMS-EPI is rapidly growing due to the enormous advantages in terms of both temporal efficiency and temporal resolution it affords.
Previously, it has been demonstrated that conventional techniques employed for ghost correction fail in regions near to steep spatial gradients of magnetic susceptibility (e.g., near the air-tissue interfaces proximal to the frontal sinuses and ear canals bordering the brain). These failures were attributed to spatially nonlinear phase errors, which conventional ghost correction techniques—based on calculating spatially linear phase errors from navigator signals—were not designed to address.
By embedding ghost correction into an image reconstruction technique utilized for in-plane accelerated parallel imaging reconstruction (e.g., GRAPPA) could successfully remove these nonlinear phase errors while concurrently performing the image reconstruction necessary for in-plane accelerated EPI. This technique, which was referred to as dual-polarity GRAPPA (“DPG”), provided improved quality image reconstructions in otherwise difficult-to-image regions near air-tissue interfaces.
The basis of the DPG technique was to acquire two frames of calibration data with equal but opposite polarity readout gradients. In this data, EPI phase errors will cancel, from which a ghost-free image was calculated using the GESTE method. To form a set of reconstruction kernels, the two frames were interleaved to form two source images—one each from the different readout polarities—then, GRAPPA kernels were trained using these two frames as source data together with the ghost-free data as a target. This technique was developed for conventional, in-plane accelerated EPI, however.
Thus, there remains a need to provide a method for reconstructing ghost-free images from data acquired using SMS acquisitions, such as SMS-EPI acquisitions.